Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis

Xia, Jing; Chen, Shuhan; Li, Yaping; Li, Hua; Gan, Minghong; Wu, Jiashuo; Prohaska, Clare C.; Bai, Yang; Gao, Lu; Gu, Leyi; Zhang, Dongfang

doi:10.3389/fimmu.2022.798538

Cited by 10 publications

(9 citation statements)

References 123 publications

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“…One previous study provided objective neuroimaging evidence for the coexistence of recoverable and long‐term unrecovered changes in the brain at 10‐month after acute COVID‐19 22 . Moreover, COVID‐19 has been found to lead to psychiatric illness 23 . Our study suggests that genetic predisposition to a higher COVID‐19 reduced childhood intelligence.…”

Section: Discussionmentioning

confidence: 58%

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Mendelian randomization study on the causal effects of COVID‐19 on childhood intelligence

Zhu

Zhou

et al. 2022

Journal of Medical Virology

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Although individuals with coronavirus disease 2019 (COVID‐19) are known to be at increased risk for other conditions resulting from pathogenic changes (including metaplastic or anaplastic) in the lungs and other organs and organ systems, it is still unknown whether COVID‐19 affects childhood intelligence. The present two‐sample Mendelian randomization study aims to identify the genetic causal link between COVID‐19 and childhood intelligence. Four COVID‐19 genetic instrumental variants (IVs) were chosen from the largest genome‐wide association studies (GWAS) for COVID‐19 (hospitalized vs. population) (6406 cases and 902 088 controls of European ancestry). The largest childhood intelligence GWAS ( n = 12 441 individuals of European ancestry) was used to evaluate the effect of the identified COVID‐19‐associated genetic IVs on childhood intelligence. We found that as the genetic susceptibility to COVID‐19 increased, childhood intelligence followed a decreasing trend, according to mr_egger ( β = −0.156; p = 0.601; odds ratio [OR] = 0.856; 95% confidence interval [CI]: 0.522–1.405), simple mode ( β = −0.126; p = 0.240; OR = 0.882; 95% CI: 0.745–1.044), and weighted mode ( β = −0.121; p = 0.226; OR = 0.886; 95% CI: 0.758–1.036) analyses. This trend was further demonstrated by the weighted median ( β = −0.134; p = 0.031; OR = 0.875; 95% CI: 0.774–0.988) and the inverse variance weighted ( β = −0.152; p = 0.004; OR = 0.859; 95% CI: 0.776–0.952). Our analysis suggests a causal link between genetically increased COVID‐19 and decreased childhood intelligence. Thus, COVID‐19 may be a risk factor for declines in childhood intelligence.

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Section: Discussionmentioning

confidence: 58%

“…22 Moreover, COVID‐19 has been found to lead to psychiatric illness. 23 Our study suggests that genetic predisposition to a higher COVID‐19 reduced childhood intelligence.…”

Section: Discussionmentioning

confidence: 61%

Mendelian randomization study on the causal effects of COVID‐19 on childhood intelligence

Zhu

Zhou

et al. 2022

Journal of Medical Virology

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“…A chemical–protein interaction network is an important research tool for understanding the function of proteins, which is helpful for advancing drug discovery ( 29 ). In the aspects of common DEGs as potential drug targets in COVID-19 and sepsis, the candidate drugs were identified by using Enrichr based on transcriptome signatures from the DSigDB database.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, to further explore the potential pathways and molecular biological functions that ITGAM may affect in COVID-19, gene set enrichment analysis (GSEA) for ITGAM was performed in GSE147507 discovery dataset. GSEA includes pathway analysis and gene ontology analysis, which plays an essential role in extracting biological insight from genome-scale experiments ( 29 , 30 ). Furthermore, immune correlation analysis of ITGAM was conducted.…”

Section: Methodsmentioning

confidence: 99%

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Liu

Gui

et al. 2022

Front. Immunol.

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Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

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“…Pathophysiological features involving the hypothalamic–pituitary–adrenal (HPA) axis, autonomic nervous system and immunological response may increase the patients’ neurobiological vulnerability to the disease [ 11 , 12 ]. Recent analyses have identified gene clusters and inflammatory signalling pathways shared between COVID-19 and mental disorders that suggest common pathogenic mechanisms [ 13 , 14 ]. In turn, people with pre-existing depression and anxiety were expected to present a worsening of their mental health status after the first outbreak of the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

Mental health and COVID-19 in a general population cohort in Spain (COVICAT study)

Goldberg

Castaño‐Vinyals

Espinosa

et al. 2022

Soc Psychiatry Psychiatr Epidemiol

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Purpose Mental health conditions may affect outcome of COVID-19 disease, while exposure to stressors during the pandemic may impact mental health. The purpose of this study was to examine these factors in relation to ocurrence of depression and anxiety after the first outbreak in Spain. Methods We contacted 9515 participants from a population-based cohort study in Catalonia between May and October 2020. We drew blood samples to establish infection to the virus. Pre-pandemic mental health conditions were confirmed through Electronic Health Registries. We used the Hospital Anxiety and Depression Scale to assess severe depression and anxiety post-pandemic. Exposure to proximal, financial and wider environment stressors during the lockdown were collected. We calculated Relative Risks (RR), adjusting for individual- and contextual covariates. Results Pre-pandemic mental health disorders were not associated with SARS-CoV-2 infection , but were associated with severity of COVID-19 disease. People with pre-existing mental health disorders showed higher prevalence of severe depression (25.4%) and anxiety (37.8%) than those without prior mental disorders (4.9% and 10.1%). Living alone was a strong predictor of severe depression among mental health patients (RR = 1.6, 95% CI 1.2–2.2). Among those without prior mental health disorders, post-lockdown depression and anxiety were associated with household interpersonal conflicts (RR = 2.6, 95% CI 2.1–3.1; RR = 2.1, 95% CI 1.9–2.4) and financial instability (RR = 2.2, 95% CI 1.8–2.9; 1.9, 95% CI 1.6–2.2). Conclusions The COVID-19 pandemic and the lockdown were associated with increased post-lockdown depression and anxiety. Patients with pre-existing mental health conditions are a vulnerable group for severe COVID-19 disease.

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Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis

Cited by 10 publications

References 123 publications

Mendelian randomization study on the causal effects of COVID‐19 on childhood intelligence

Mendelian randomization study on the causal effects of COVID‐19 on childhood intelligence

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Mental health and COVID-19 in a general population cohort in Spain (COVICAT study)

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